Tradeoffs in automated financial regulation of decentralized finance due to limits on mutable turing machines
Saved in:
| Published in: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 3016 |
|---|---|
| Published: |
Nature Publishing Group
|
| Subjects: | |
| Online Access: | Citation/Abstract Full Text - PDF |
| Tags: |
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3158989667 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-024-84612-9 |2 doi | |
| 035 | |a 3158989667 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 245 | 1 | |a Tradeoffs in automated financial regulation of decentralized finance due to limits on mutable turing machines | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a We examine which decentralized finance architectures enable meaningful regulation by combining financial and computational theory. We show via deduction that a decentralized and permissionless Turing-complete system cannot provably comply with regulations concerning anti-money laundering, know-your-client obligations, some securities restrictions and forms of exchange control. Any system that claims to follow regulations must choose either a form of permission or a less-than-Turing-complete update facility. Compliant decentralized systems can be constructed only by compromising on the richness of permissible changes. Regulatory authorities must accept new tradeoffs that limit their enforcement powers if they want to approve permissionless platforms formally. Our analysis demonstrates that the fundamental constraints of computation theory have direct implications for financial regulation. By mapping regulatory requirements onto computational models, we characterize which types of automated compliance are achievable and which are provably impossible. This framework allows us to move beyond traditional debates about regulatory effectiveness to establish concrete boundaries for automated enforcement. | |
| 653 | |a Enforcement | ||
| 653 | |a Computer applications | ||
| 653 | |a Automation | ||
| 653 | |a Mathematical models | ||
| 653 | |a Regulation of financial institutions | ||
| 653 | |a Foreign exchange controls | ||
| 653 | |a Bans | ||
| 653 | |a Financial systems | ||
| 653 | |a Compliance | ||
| 653 | |a Algorithms | ||
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 3016 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3158989667/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3158989667/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |